A feature tensor with height \(h\) , width \(w\) , and the number of channels \(c\)  will be denoted \(\mathcal {X} \in \mathbb {R}^{h \times w \times c}\) . An illustration of a feature tensor extracted from layer add_1 of ResNet-18 [1]} is shown in Fig. REF .
Layer add_1 corresponds to the output of the shortcut connection and element-wise addition applied to the final output of the conv2_x layers, as denoted in [1]}. Currently, there is no “standard" approach to packetizing deep feature tensor data. DFTS2 employs a packetization scheme inspired by a common practice in video streaming, where video frames are encoded into packets of consecutive rows of frame elements [3]}.
Similarly, each channel in a DFTS2 tensor is partitioned into groups of \(r_p\)  rows, as indicated by the dashed
green lines in Fig. REF . Zero padding is done on the last packet of a channel if the number of channel rows (\(h\) ) is not an integer multiple of the number of packet rows (\(r_p\) ). The user-defined \(r_p\)  was set to 8 rows in our experiments. Packetized tensors were min-max quantized [4]} to 8 bits/element. While it is possible to further compress the data by additional prediction, quantization, entropy coding [5]}, [6]}, [7]}, [8]}, and/or bit allocation [9]}, this was not done in our experiments because the focus is on error resilience, not on compression.
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